Big data & machine learning
Leveraging big data and machine learning to transform the insurance industry

11th August 2017

Over the past 12 months, big data and machine learning innovations have dominated the headlines, and for good reason - when combined, both technologies have huge potential to transform the way we do business.

According to the latest PwC Global Fintech Report, 84% of insurers plan to invest in data analytics within the next twelve months. Insurance retailers are already beginning to see the benefits of the technology: one Japanese insurance firm has employed a machine learning system to analyse and interpret relevant data to calculate payouts to customers.

But beyond automation, how can the insurance industry benefit from big data and machine learning?

How does machine learning work?
Machine learning is an area within artificial intelligence (AI) that relies on big data to learn how to perform a certain task automatically. The technology does this by extracting information from various datasets to discover the best way to accurately perform the designated task. Once it has learnt to complete the task to a high standard, it continues to learn and improve its performance through repetition by being fed fresh data.

Google's image recognition software, Cloud Vision, uses a machine learning model similar to that of Google Photos and Search by Image to label the content of images. The software was 'trained' by analysing a large data set of public photos, and uses this experience to not only label the focus of the image, but also the context surrounding it.

Predictive risk analysis
In the insurance sector, machine learning is most often used as a tool to predict risk, drawing from a huge amount of data to inform more accurate pricing and underwriting. As mineable data sources expand to include social media, wearables and devices connected to the internet of things, insurers have a huge opportunity to form a wider customer view by feeding this data to a machine learning AI to more effectively calculate risk.

Big data analytics and machine learning enable insurance companies to drill down into information and gain insights at a speed and scale that would be impossible for a human analyst to match. The analytics and reports can not only inform marketing decisions, but also help insurance retailers to better understand customer requirements and develop products to meet these needs.

Improved fraud detection
As explored in one of our previous blog posts, big data and machine learning can be a seriously powerful tool for detecting fraud. According to the ABI, insurance fraud comes at a cost to the industry of £1.3 billion worth of detected fraud cases and £2.1 billion worth of undetected cases, and adds an extra £50 to UK consumers' annual policy bills.

CDL's Hummingbird data intelligence solution, for example, is capable of highlighting fraudulent activity in sub-seconds by identifying behavioural patterns across datasets and enabling retailers to price out and prevent fraudulent actions at point of quote.

Challenges facing uptake
When leveraging the combined power of big data and machine learning, it is important to remember that transparency is key. New regulation such as General Data Protection Regulation (GDPR) will give consumers additional protections and control over their personal data, which will make it vital for insurance retailers to be upfront about how and why they are using customer data.

In order to take advantage of big data and machine learning, businesses must be aware of the new regulatory environment and clearly communicate how they use customer data. Both technologies bring a huge range of benefits to the insurance sector, and there is no doubt that the future holds more innovative machine learning and big data solutions that will transform how the industry operates.